The application of radiation to an individual is commonplace in a number of industries, such as the medical industry, where radiation is used in imaging applications and therapy applications, and the security industry, where radiation is used to perform inspection processes. Regardless of the industry, whenever radiation is applied to a living subject, a balance is sought between the ability to achieve the best results of the process utilizing the radiation, which typically leans toward increased radiation dosage, and the safety of the subject, which typically leans toward decreased radiation dosage.
The following references provide guidance: Intensity Modulated Radiotherapy: A Large Scale Multi Criteria Programming Problem, by authors Kuefer, Monz, Scherrer, Trinkaus, Bortfeld, Thieke, OR Spektrum, 2003; Unbiased Approximation in Multicriteria Optimization, by Klamroth, Tind, and Wiecek, Vol. 56 (3), 2002, pages 413 to 437 (herein Klamroth et al.); Approximation Methods in Multiobjective Programming, by Ruzika and Wiecek, Journal of Optimization Theory and Applications, Vol. 126, 2005, pages 473 to 501 (herein Ruzika et al.) and Approximating the Noninferior Set in Multiobjective Linear Programming Problems, by Solanki, European Journal of Operational Research, Vol. 68, 1993, pages 356 to 373 (herein Solanki).
U.S. Pat. No. 8,489,366 B2 (Craft and Bortfeld) discloses a method for discovering a Pareto surface but will fail when the surface has at least one concave portion (bent inwardly). It operates according to triangles that are placed around a small segment of a Pareto surface, and then iterates further steps to further define the unknown surface.
Within radiotherapy treatment planning, a search is motivated for a patient-specific best compromise between applying sufficient dose to the target and sparing organs at risk and healthy tissue. Typically, a list of clinical goals (“treatment protocol”) is aspired to, containing (minimally required to optimal) target doses for specific volume quantiles of the planning structures, or reversely target quantiles for specific dose levels.
In conventional planning, (surrogate) objectives for the different aspects are weighted and combined in a single objective function, and the plan is optimized with respect to this function. However, the best weighting factors for the specific patient are not known a-priori and must be found out by the planner by trial-and-error (“human iteration loop”).
Therefore it would be desirable to have a system and method that allows treatment planners and physicians to directly and systematically explore the range of treatment options with respect to the criteria given by the treatment protocol. This would allow them to understand the trade-offs for the individual patient while avoiding the time-consuming human iteration loop.